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I'm working on a machine learning application for reading data from fuel pumps, so far I've gone ahead and created a pretty robust YOLOv5 Object Detection Model that can detect the regions that I want fairly accurately. But there is a problem, at certain times of the day there are reflections on the digital screen and I'm unable to use OpenCV pre-process it so that I can extract the numbers from the display.

Check this Video to Understand (YOLOv5 Detection)

https://www.youtube.com/watch?v=3XjZ6Nw70j8

Minimum Reproduceable Example

Cars come and go and their reflection makes it really difficult to differentiate between the reigons for digital-7 font that is used in these displays, you can check out the following repository to understand what I want as s result https://github.com/arturaugusto/display_ocr

Example Image I encounter

Other Solutions I'm Open to:

  1. Since, this application is going to run 24/7 how should I deal with different times, perhaps create a database of HSV ranges to extract at different times.

  2. Use a polarizing lens would it help in removing the reflections (any user's who have had previous experiences in deploying them).

Edit: I added the correct video ...

Muneeb Ahmad Khurram
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  • talk to the manufacturer of those pumps. they'll surely give you a way to read the data directly. – Christoph Rackwitz Sep 29 '22 at 09:40
  • @ChristophRackwitz Thanks. I'm really hoping on a polarizing lens, if it works then I'll be back in business. I'll post an update about that. – Muneeb Ahmad Khurram Sep 29 '22 at 09:50
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    I'm not an expert, but as far as I know polarising lenses need to be calibrated for a certain distance. As objects move further or closer than a calibrated distance, the polarising lense will be less effective. Just something to keep in mind, you should be fine as I assume most of your iamges will be fairly close. – MJC Sep 29 '22 at 13:02
  • Why can you not process that image to give it more contrast. Seems like a simple issue. – fmw42 Sep 29 '22 at 16:01

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